Research Article: 2021 Vol: 27 Issue: 6
Mpelegeng Veneza Sechabe, University of Limpopo
Olawale Fatoki, University of Limpopo
Citation Information: Sechabe, M.V., & Fatoki, O. (2021). Determinants of electronic marketing adoption by small and medium enterprises in South Africa. Academy of Entrepreneurship Journal, 27(6), 1-12.
Small and medium enterprises (SMEs) play an important role in the economy as they contribute to job creation, poverty alleviation, innovation, economic growth, and development. However, they tend to fail due to business challenges such as poor performance. One of the solutions to business failure is the adoption of electronic marketing (E-marketing) which consequently leads to improved performance. This purpose of this study was to examine the determinants of the adoption of E-Marketing and the relationship between E-Marketing adoption and the performance of SMEs. The study adopted the quantitative research design, and the cross-sectional survey method was used to collect data from 156 SMEs. The Partial Least Square Structural Equation Modelling (PLS SEM) was used for data analysis. The results indicated a significant positive relationship between the adoption of E-Marketing and the performance of SMEs.
Electronic Marketing, Determinants, Performance, Small and Medium Enterprises, South Africa.
Small and medium enterprises (SMEs) are widely viewed as having a significant role to play in the socio-economic development of many countries around the world. Governments around the world thereby focus on improving the SME sector to support economic growth and reduce unemployment, poverty and income inequality. SMEs make up approximately 98.5% businesses in South Africa. In addition, SMEs contribute approximately 36% of gross domestic product and 66% of South Africa's employment (Small Business Institute, 2019; Small Enterprise Development Agency, 2019). Furthermore, the government has identified SMEs as a priority for job creation to minimise the high unemployment rate estimated at 34.4% (Statistics South Africa, 2021). The failure rate of SMEs is very high not only in South Africa but globally. Between 50 and 70% of new SMEs fail within the first five years. The adoption of electronic marketing (E-marketing) can have a significant effect on the performance of SMEs”. E-marketing is broader than online marketing because it covers not only electronic platforms such as the Internet, e-mail and wireless platforms, but also online users, knowledge management and electronic customer service”.
Shah and Mohd “argue that businesses, including SMEs, are being pushed to adopt E-marketing technology to succeed in the new economy and adopters will leave those companies which do not adopt E-marketing behind. SMEs need to get into the game of E-marketing or risk being shut out of a critical marketplace. Sellers, customers, and government pay much attention to motivating SMEs to adopt the E-marketing practice. “Therefore, it is important to understand the factors that can impact the adoption of E-marketing by SMEs. Despite the various studies on the aspects of adoption of E-marketing, its effect on the performance of SMEs remains uncertain and the findings of empirical studies are inconclusive (Alrousan et al., 2020). The study has two objectives. First, the study will determine the determinants of E- marketing by SMEs. Second, the study will examine the relationship between E-marketing adoption and the performance of SMEs. The objectives of this research project will be achieved through the use of The Technology Accepted model (TAM). The original TAM model by Davis consists of perceived ease of use, perceived usefulness, attitude towards using, behavioural intention to use, actual usage and external variables (González-Loureiro & Pita-Castelo, 2012). In this research study, the model was extended by adding the age of the owner, the owner’s level of education, knowledge of the internet, the size of the firm, compatibility, competitive pressure, customer pressure/supplier pressure, government support and performance. In addition, financial and marketing dimensions to measure firm performance.
E-marketing
E-marketing is a modern business practice that involves the purchase and sale of goods, services, information, and ideas over the Internet and other electronic channels. E-marketing means the marketing of products or services over the Internet. The terms E-marketing, online marketing and Internet marketing are generally interchanged and can be considered as synonymous. According to Barabanova, E-marketing includes mobile phones, Intranet and Extranet environment, and emails.
Technology Acceptance Model (TAM)
The Technology Acceptance Model (TAM) is proposed by Davis to explain the acceptance, usage, and adoption of information technology”. The model had been tested for more than two decades in the adoption of different technologies and is a successful model in predicting and explaining behaviour across a wide variety of domains” (Qashou & Saleh, 2018; Mooya & Phiri, 2021). TAM suggests that when users are presented with new technology, different variables influence the decision whether and how they will use it. Two causal linkages influence this decision: perceived usefulness (PU) and perceived ease of use (PEOU) of the relevant technology (Mahadea & Kaseeram, 2018). The model provides explanations of determinants of computer technology acceptance by tracing the impact of external factors on internal beliefs, intentions and attitudes (Qashou & Saleh, 2018).
Determinants of E-marketing adoption
Perceived usefulness
Perceived usefulness is defined as the degree to which a person believes that using a particular system would enhance his or her job performance. Perceived usefulness is a factor that can affect the intention of an enterprise to adopt E-marketing (Katua, 2014). The study by Taylor and Strutton (2018) showed that PU has both direct and indirect effect on the intention to use E-Marketing. Drawing on the Technology Acceptance model (TAM), It is hypothesised that:
H1: Perceived usefulness has a significant impact on SMEs’ attitude towards E-marketing adoption.
H2: Perceived usefulness has a significant impact on SMEs’ behavioural intention to adopt E- marketing.
Perceived ease of use (PEOU)
Perceived ease of use refers to the degree to which a person believes that using a particular system would be free of effort. PEOU has a strong and significant influence on the perceived usefulness of E-marketing because advancements in technology and ease of use contribute to positive performance (Nuryanto et al., 2020). PEOU has also been shown to influence behaviour through two causal ways: (1) a direct effect on behaviour and (2) an indirect effect on behaviour via PU” (Matikiti et al., 2018). It is hypothesised that:
H3: Perceived ease of use has a significant impact on SMEs’ attitude towards E-marketing adoption.
H4: Perceived ease of use has a significant impact on perceived usefulness.
Age of the owner
Elderly executives have been conditioned to accept existing organisational situations and routines and have an increased mental dedication to them; as a result, they will be less willing to dedicate themselves to altering them.” On the other hand, younger managers usually have greater learning capabilities and more recent education and are therefore likely to be more risk-taking and flexible” (Kitchell, 2018; Damanpour & Schneider, 2006). It is hypothesised that:
H5: The age of the owner has a significant impact on SMEs’ behavioural intention to adopt E-marketing.
Level of education of the owner
The level of education, knowledge and skills can affect the adoption of a technology. According to Aspasia and Ourania and Wamba and Carter, the quality of knowledge of managers has a substantial impact on the adoption of E-marketing. On the other hand, Gong could not find any relationship between the level of education and behavioural intention towards using E-marketing platforms”. It is therefore hypothesised that:
H6: The owner’s level of education has a significant impact on SMEs’ behavioural intention to adopt E-Marketing.
Knowledge of the internet
The literature points out that a lack of suitably qualified internal IT innovation experts has serious repercussions for IT sophistication in firms (Thong, 2016). It is to be expected that the availability of competent staff with the requisite IT or internet skills will stimulate SMEs’ behavioural intention to adopt E-marketing. It is hypothesised in this study that:
H7: Knowledge of the internet has a significant impact on SME’s behavioural intention to adopt E-Marketing.
Size of the firm
According to Rahayu and Day (2017), the firm size is a vital determinant factor in E-marketing adoption because it determines the institution's ability to provide financial and human resources that are required to adopt and implement this technology. The greater the size of the enterprise, the greater the ability to provide these resources and thus the greater their ability to adopt E-marketing. It is hypothesised that:
H8: The size of the firm has a significant impact on SMEs’ behavioural intention to adopt E-Marketing.
Competitive pressure
Competitive pressure can affect the need to adopt E-Marketing (Stockdale & Standing, 2017). The greater the rate of competition in the market, the more likely a firm will need to adopt E-marketing. “In a model developed to ascertain the determinants of E-Marketing adoption, Chwelo concluded that external pressure, including competitive pressure, is a significant antecedent of the intention to use-E-marketing. Competitive pressure convinces firms to develop a positive behavioural intention toward an innovation. It is hypothesised that:
H9: Competitive pressure has a significant impact on SMEs’ behavioural intention to adopt E-Marketing.
Customer/supplier pressure
Satisfying the diverse needs and expectations of customers and suppliers through the use of electronic services that facilitate better interactive communication with customers is a major driver of E-marketing adoption. Organisations are forced to adopt and use IT innovations because they believe that their customers expect them to do so”. Therefore, customers’ expectations may induce a positive adoption intention among SMEs. It is hypothesised that:
H10: Customer pressure has a significant impact on SMEs’ behavioural intention to adopt E-marketing.
Government support
Government support is equally important for E-marketing adoption by SMEs. Lack of government support can inhibit the adoption of E-marketing by SMEs (Qashou & Saleh, 2018). Many SMEs do not have access to the debt and equity markets and can only obtain the necessary funds for the adoption of new technology from government (Alrousan et al., 2020). It is hypothesised that:
H11: Government support has a significant impact on SMEs’ behavioural intention to adopt E-Marketing.
Compatibility
Rogers (1995) defines technology compatibility as the degree to which the Internet is consistent with the experience, values and needs of the adopter. More compatibility of an innovation means less confusion for the potential adapter (Alrousan et al., 2020). Studies by Nguyen et al. (2015); Qashou and Saleh (2017) found a significant positive relationship between compatibility and E-marketing adoption. The higher the level of compatibility, the higher a user’s interest in adopting a new technology. It is hypothesised that:
H12: Compatibility has a significant impact on SMEs’ behavioural intention to adopt E-marketing.
Attitudes towards adopting E-Marketing
The attitude toward a new technology influences behavioural intention to adopt it (Alrousan et al., 2020). Qashou & Saleh (2017) concur that attitude is an important determinant that affects the intention to continue using E-marketing. It is hypothesised that:
H13: Attitude has a significant impact on SMEs’ behavioural intention to adopt E-marketing.
Behavioural intention to use E-marketing
An extremely low behavioural intention indicates a decision against the behaviour, whereas an extremely high behavioural intention indicates a decision in favour of the behaviour (Nguyen et al., 2018; Qashou & Saleh, 2017; Kumar et al., 2019). SMEs owners with high behavioural intention will more likely use E-marketing compared to those with low behavioural intention. It is hypothesised that:
H14: Behavioural intention has a significant impact on the actual usage of E-marketing.
Actual usage
The use of E-marketing benefits not only big businesses, but also SMEs in improving their performance (Qashou & Saleh, 2017). E-marketing usage increases customer base, improves efficiency and effectiveness, reduces costs and improves communication and cooperation among employees. It is hypothesised that:
H15: Actual use of E-marketing has a significant impact on the performance of SMEs.
The study utilised the quantitative research design. Data was collected from respondents who are owners of SMEs through the cross-sectional survey method. According to the National Small Business Act of South Africa (2019), a micro enterprise will have between 0-10 employees, a small enterprise between 11-50 employees and medium-sized 51-250 employees. The survey was conducted in Polokwane local Municipality in the Limpopo Province of South Africa. Before the actual survey, the questionnaire was pre-tested with twenty owners of SMEs, who were later excluded from participating in the main research. The results of the pre-study resulted in the removal of participants’ names and places of work in the demographic information section. This was done because participants were sensitive to disclose information to the researcher. “The questionnaire of this study was divided into four sections”, namely: (1) demographic information, (2) the firm’s characteristics and (3) determinants of electronic marketing adoption and (4) performance. The convenience sampling method was used to select the participating in the study areas. This is because it was difficult to obtain a formal sampling frame of SMEs in the study area of the study and that participation is voluntary. The study employed the Partial Least Square Structural Equation modelling for analysis. The Cronbach’s alpha was used as a measure of reliability.
250 questionnaires were distributed to SMEs owners in Polokwane Local Municipality and 156 questionnaires were returned and found usable. The respondents were (90) females and (66) males. The majority of the respondents (85) have Matric qualification, (43) post Matric and (28) pre matric qualifications. The majority of the respondents that participated in the survey are between 31-40 years (40), 21-30 years (49), and 41-50 (38). In addition, the majority of the respondents (168) have been with the SMME for 1-5 years, while (51) 5-10 years and (11) respondents 10-15 years.
Measurement Model
Fornell and Larcker proposed the traditional metric and suggested that each construct’s AVE should be compared to the squared inter-construct correlation (as a measure of shared variance) of that same construct and all other reflectively measured constructs in the structural model. The shared variance for all model constructs should not be larger than their AVEs. From the table below, the diagonals in bold and italics represent the square root of the average variance extracted (AVE), while the other entries represent the correlations. In addition, the table shows that the square root of AVE is higher than the correlations among the latent variables. The meeting of the two requirements indicates an adequate level of discriminant validity is shown in Table 1 & Table 2. All these tests confirm that the measurement model is satisfactory.
| Table 1 Measurement Model | |||||
| Construct | Measurement items | Loadings | Cronbach’s Alpha | Composite reliability | AVE |
| Perceived usefulness | 0.927 | 0.946 | 0.874 | ||
| PU1 | 0.919 | ||||
| PU2 | 0.929 | ||||
| PU3 | 0.954 | ||||
| PU4 | 0.940 | ||||
| PU5 | 0.932 | ||||
| Perceived ease of use | 0.928 | 0.947 | 0.915 | ||
| PEOU1 | 0.955 | ||||
| PEOU2 | 0.962 | ||||
| PEOU3 | 0.970 | ||||
| PEOU4 | 0.918 | ||||
| PEOU5 | 0.978 | ||||
| Knowledge of the internet | 0.920 | 0.939 | 0.756 | ||
| KO1 | 0.867 | ||||
| KO2 | 0.875 | ||||
| KO3 | 0.876 | ||||
| KO4 | 0.824 | ||||
| KO5 | 0.903 | ||||
| Competitive pressure | 0.726 | 0.836 | 0.559 | ||
| CP1 | 0.893 | ||||
| CP2 | 0.889 | ||||
| CP3 | 0.806 | ||||
| Government support | 0.821 | 0.878 | 0.719 | ||
| GS1 | 0.746 | ||||
| GS2 | 0.868 | ||||
| GS3 | 0.798 | ||||
| GS4 | 0.834 | ||||
| GS5 | 0.940 | ||||
| GS6 | 0.890 | ||||
| Customer pressure | 0.761 | 0.935 | 0.675 | ||
| CUP1 | 0.806 | ||||
| CUP2 | 0.905 | ||||
| CUP3 | 0.876 | ||||
| CUP4 | 0.832 | ||||
| CUP5 | 0.906 | ||||
| Compatibility | 0.908 | 0.917 | 0.867 | ||
| COM1 | 0.940 | ||||
| COM2 | 0.935 | ||||
| COM3 | 0.974 | ||||
| COM4 | 0.979 | ||||
| Attitude | AT1 | 0.952 | 0.895 | 0.930 | 0.775 |
| AT2 | 0.958 | ||||
| AT3 | 0.961 | ||||
| Behavioural intention | 0.906 | 0.944 | 0.830 | ||
| BI1 | 0.979 | ||||
| BI2 | 0.949 | ||||
| BI3 | 0.978 | ||||
| BI4 | 0.976 | ||||
| Actual usage | 0.731 | 0.835 | 0.641 | ||
| AU1 | 0.831 | ||||
| AU2 | 0.892 | ||||
| AU3 | 0.940 | ||||
| AU4 | 0.945 | ||||
| AU5 | 0.950 | ||||
| Performance | 0.921 | 0.937 | 0.821 | ||
| FP1 | 0.901 | ||||
| FP2 | 0.727 | ||||
| MP1 | 0.919 | ||||
| MP2 | 0.896 | ||||
| MP3 | 0.858 | ||||
| MP4 | 0.911 | ||||
| MP5 | 0.929 | ||||
| MP6 | 0.932 | ||||
| MP7 | 0.901 | ||||
| Table 2 Discriminant Validity | |||||||||||
| AT | AU | BI | COM | CP | CUP | GS | KOI | PEOU | PU | PF | |
| AT | 0.880 | ||||||||||
| AU | 0.755 | 0.80 | |||||||||
| BI | 0.866 | 0.776 | 0.911 | ||||||||
| COM | 0.769 | 0.753 | 0.784 | 0.931 | |||||||
| CP | 0.684 | 0.589 | 0.731 | 0.651 | 0.747 | ||||||
| CUP | 0.518 | 0.536 | 0.520 | 0.714 | 0.451 | 0.822 | |||||
| GS | 0.366 | 0.449 | 0.280 | 0.250 | 0.159 | 0.127 | 0.847 | ||||
| KOI | 0.789 | 0.810 | 0.841 | 0.718 | 0.603 | 0.528 | 0.267 | 0.869 | |||
| PEOU | 0.905 | 0.790 | 0.894 | 0.769 | 0.642 | 0.534 | 0.350 | 0.835 | 0.956 | ||
| PU | 0.907 | 0.783 | 0.887 | 0.752 | 0.656 | 0.477 | 0.342 | 0.770 | 0.864 | 0.935 | |
| PF | 0.739 | 0.727 | 0.743 | 0.822 | 0.638 | 0.560 | 0.191 | 0.711 | 0.751 | 0.715 | 0.906 |
Structural Model
To assess the structural model, the common method bias, the goodness of fit, the R2, the Q2 and the effect size were evaluated in line with the requirements of Hair. The variance inflation factor (VIF) was used to test the existence of common method bias (CMB). The VIF values for this research study are ,1.35(AT) ,2.05 (AU), 1.05 (BI) ,2.12 (COM) ,2.07 (CP), 2.11(CUP) ,1.18 (GS), 2.98 (KOI), 2. 95 (PEOU), 2.85 (PU) and 1.95 (PF). The VIF values of the model constructs are less than 3.3 in this research study. This means that this study is free from CMB. According to Hair. (2019) R2 values of 0.75, 0.50 and 0.25 can be considered substantial, moderate, and weak. This research study has substantial (AT-0.723, AU-0.603, BI-0.701) and moderate (PF-0.531) R- squared. The Q2 values of this model are AT= 0.653; AU= 0.494; BI =0.723 and PF=0. 404. The Q2 values of this models are greater than 0 and therefore suggests that hat the model has adequate predictive power. In the guidelines for assessing f2. Hair point out that values of 0.02, 0.15, and 0.35, respectively, depict small, medium, and large effects of an exogenous latent variable on an endogenous latent variable. In addition, effect size values that are less than 0.02 indicate that there is no effect. The effect size values of this model are AT=0.055; AU = 0.09; BI =0.024, KOI=0.228; COM=0.25; CP= 0.097; PU= 0.281; and PEOU=0.264. The values for the SRMR range from zero to 1.0 with well-fitting models obtaining values less than 0.05.A lower SRMR indicates a better fit. The SRMR value obtained in this study is 0.035 which shows that the model has a good fit.
Table 3 depicts the results of the structural model. The results of these relationships are as follows: H1 =(SB=0.491;T=8.03; P<0.05) ;H2 ( SB=0.213 ; T= 4.001 , P<0.05) ;H3 ( SB=0.481 ; T= 8.344 , P<0.05) ;H4 ( SB= O.872; T=30.141 , P<0.05) , H5 ( SB=-0.061 ;T=1.262 , P>0.05) ; H6 (SB=-0.020 ;T=0.371 , P>0.05) ; H7 (SB= 2.84 ; T=6.106 , P<0.05) ; H8 (SB=-0.032; T=0.741; P>0.05) ; H9 (SB=0.175;T=2.469; P<0.05) ; H10 (SB=-0.034 ;T=0.937 ; P<0.05) ; H11(SB=-0.130 ;T=0.235 ; P>0.05) , H12 (SB=0.130 ;T=1.686 P<0.05) ; H13(SB=0.198; T=1.590 P<0.05); H14(SB=0.776; T=19.692; P<0.05) ; H15( SB=0.705 ;T=17.096 ; P<0.05).Drawing from these results H1 , H2 , H3 ,H4 , H7 , H9 , H10 ; ,H12 ;H13 , H14 and H15 are accepted , whereas H5 ,H6 , H8 and H11, rejected .”These results show that there is a significant relationship between some of the variable the variables and no relationship between other variables”.
| Table 3 Path Coefficient and T-Statistics (5%) | |||
| Hypothesis | Standard Beta | T-statistics | Decision |
| PU → AT | 0.491 | 8.03 | Accepted |
| PU → BI | 0.213 | 4.001 | Accepted |
| PEOU → AT | 0.481 | 8.344 | Accepted |
| PEOU → PU | 0.872 | 30.141 | Accepted |
| Age → BI | -0.061 | 1.262 | Rejected |
| Edu → BI | -0.020 | 0.371 | Rejected |
| KOI → BI | 2.84 | 6.106 | Accepted |
| SOF → BI | -0.032 | 0.741 | Rejected |
| CP → BI | 0.175 | 2.469 | Accepted |
| GS → BI | -0.034 | 0.937 | Rejected |
| CUP → BI | -0.013 | 0.235 | Rejected |
| COM → BI | 0.130 | 1.686 | Accepted |
| AT → BI | 0.198 | 1.590 | Accepted |
| BI → AU | 0.776 | 19.692 | Accepted |
| AU → PF | 0,705 | 17,096 | Accepted |
The study investigated the effect of electronic marketing adoption on the performance of SMEs. The results of this research showed that there is a significant relationship between perceived usefulness and attitude to adopt E-marketing by SMEs. PEOU has also been shown to influence behaviour (i.e., IT adoption) through two causal ways: (1) a direct effect on behaviour and (2) an indirect effect on behaviour via PU (Alzubi et al., 2018; Matikiti et al., 2018)”. Results on the relationship between the age of the owner and the behavioural intention to adopt E-marketing showed that there is no relationship between the two variables. “In examining the factors that influence E-marketing adoption by SMEs, the study revealed that there is no relationship between the owner’s level of education and SMEs’ behavioural intention to adopt E-marketing. According to Boshoff and Elliot (2015), a successful adoption and implementation of E-Marketing in a firm’s operations are partly dependent on the level of ICT and E-Marketing knowledge of key personnel”. This study also showed that there is a relationship between the knowledge of the internet and the behavioural intention of SMEs in adopting E-marketing. The study revealed that there is no significant relationship between the size of the firm and behavioural intention to adopt E-marketing. However, the hypothesis that there is a significant relationship between competitive pressure and behavioural intention of SMEs to adopt E-marketing is accepted”. “These results are consistent with the study of Qashou and Saleh, (2018) which concluded that external pressure, including competitive pressure, is a significant antecedent of the intention to use E-marketing. In summary pressure from competitors compels firms to develop a positive behavioural intention towards an innovation”. Government support is equally important for E-marketing adoption by SMEs. However, this study has revealed that there is no relationship between government support and behavioural intention. The findings of the study showed that there is no relationship between customer pressure and SMEs’ behavioural intention to adopt E-marketing. The findings of the study further revealed that there is a significant relationship between compatibility and SMEs’ behavioural intention to adopt E-marketing. The hypothesis that there is a relationship between SMEs’ attitude towards E-marketing adoption and SMEs’ behavioural intention to adopt E-marketing is supported. The results of this study showed a significant relationship between SMEs’ behavioural intention to adopt E-marketing and SMEs’ actual usage of E-Marketing. The results of this study further revealed that there is a significant relationship between actual usage of E-marketing and the performance of SMEs.
The study concludes that perceived usefulness, perceived ease of use, knowledge of the internet, competitive pressure, customer pressure, and compatibility influence E-marketing adoption by SMEs. To able to alleviate the barriers associated with the adoption of E-marketing among SMEs, it is necessary that formal education and means of awareness creation be introduced starting from national level so as the negative perception about the adoption of E-marketing will be averted”. “E-marketing can hardly be effective unless firms and individuals are educated about the opportunities that internet-related technologies offer to businesses and customers. Access to computers and internet at the workplaces is important if E-marketing is to be enhanced but this is not enough without education and training in the effective use of these tools for marketing activities. Therefore, education and training should be initiated to increase the level of information and also the computer literacy levels of owners and managers of SMEs. There is also the need to encourage stakeholder participation especially customers on the benefits and convenience of using electronic trading platforms. Players in the internet industry should also seek to strengthen the internet capacity of SMEs through the nurturing of E-marketing and the examination of the feasibility of a local/regional mechanism to promote information sharing and capacity building to enhance and increasing the efficiency and competitiveness of SMEs. The study has some limitation and proposes some areas for further research. Only 156 SMEs in one province participated in the survey and care should be exercised in generalising the findings of the study. An international study on the determinants of the adoption of E-marketing by SMEs will help to generalise the findings of this study.